GPS based high precision timestamp generation circuit for an autonomous driving vehicle

ABSTRACT

In one embodiment, a system receives, at a sensor unit, a global positioning system (GPS) pulse signal from a GPS sensor of the autonomous driving vehicle (ADV), where the GPS pulse signal is a RF signal transmitted by a satellite to the GPS sensor, where the sensor unit is coupled to a number of sensors mounted on the ADV to perceive a driving environment surrounding the ADV and to plan a path to autonomously drive the ADV. The system receives a first local oscillator signal from a local oscillator. The system synchronizes the first local oscillator signal to the GPS pulse signal in real-time, including modifying the first local oscillator signal based on the GPS pulse signal. The system generates a second oscillator signal based on the synchronized first local oscillator signal, where the second oscillator signal is used to provide a time to at least one of the sensors.

TECHNICAL FIELD

Embodiments of the present disclosure relate generally to operatingautonomous vehicles. More particularly, embodiments of the disclosurerelate to a global position system (GPS) based high precision timestampgeneration circuit for an autonomous driving vehicle.

BACKGROUND

Vehicles operating in an autonomous mode (e.g., driverless) can relieveoccupants, especially the driver, from some driving-relatedresponsibilities. When operating in an autonomous mode, the vehicle cannavigate to various locations using onboard sensors, allowing thevehicle to travel with minimal human interaction or in some caseswithout any passengers.

Motion planning and control are critical operations in autonomousdriving. The accuracy and efficiency of the motion planning and controldepends heavily on the accuracy of a time of the vehicle to timestampand synchronize different sensor inputs for the vehicle. Conventionally,a time can be generated using high precision crystal oscillators whichcan be costly and may not be available for different sensors anddevices. Furthermore, time generation from more than one clock sourcesof sensors and devices may be confusing and imprecise.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the disclosure are illustrated by way of example and notlimitation in the figures of the accompanying drawings in which likereferences indicate similar elements.

FIG. 1 is a block diagram illustrating a networked system according toone embodiment.

FIG. 2 is a block diagram illustrating an example of an autonomousvehicle according to one embodiment.

FIGS. 3A-3B are block diagrams illustrating an example of a perceptionand planning system used with an autonomous vehicle according to oneembodiment.

FIG. 4 is a block diagram illustrating architecture of an autonomousdriving system according to one embodiment.

FIG. 5 is a block diagram illustrating an example of a sensor unitaccording to one embodiment.

FIG. 6A is a block diagram illustrating an example of a high precisiontime generation unit according to one embodiment.

FIG. 6B is a block diagram illustrating an example of a high precisiontime generation unit with three counter generators according to oneembodiment.

FIG. 7 is a flow diagram of a method to generate a time according to oneembodiment.

FIG. 8 is a block diagram illustrating an example of a sensor unitaccording to one embodiment.

FIG. 9 is a block diagram illustrating an example of a timestamp synchub device according to one embodiment.

FIG. 10 is a block diagram illustrating an example of a sensor unitaccording to one embodiment.

FIG. 11 is a block diagram illustrating an example of a time sourceranking module according to one embodiment.

FIG. 12 is a flow diagram of a method to rank time sources according toone embodiment.

FIG. 13 is a block diagram illustrating an example of a sensor unitaccording to one embodiment.

FIG. 14 is a block diagram illustrating an example of a time sourcerecovery module according to one embodiment.

FIG. 15 is a time chart illustrating an example of a smooth time sourcerecovery according to one embodiment.

FIG. 16 is a flow diagram of a method to recover a time source accordingto one embodiment.

FIG. 17 is a block diagram illustrating a data processing systemaccording to one embodiment.

DETAILED DESCRIPTION

Various embodiments and aspects of the disclosures will be describedwith reference to details discussed below, and the accompanying drawingswill illustrate the various embodiments. The following description anddrawings are illustrative of the disclosure and are not to be construedas limiting the disclosure. Numerous specific details are described toprovide a thorough understanding of various embodiments of the presentdisclosure. However, in certain instances, well-known or conventionaldetails are not described in order to provide a concise discussion ofembodiments of the present disclosures.

Reference in the specification to “one embodiment” or “an embodiment”means that a particular feature, structure, or characteristic describedin conjunction with the embodiment can be included in at least oneembodiment of the disclosure. The appearances of the phrase “in oneembodiment” in various places in the specification do not necessarilyall refer to the same embodiment.

A real-time clock (RTC) is a computer clock that keeps track of time.RTCs are present in almost any electronic device which needs to keepaccurate time. Most RTCs use a crystal oscillator such as a quartzcrystal. A crystal oscillator is an electronic oscillator integratedcircuit (IC) which is used for the mechanical resonance of a vibratingcrystal of piezoelectric material. It will create an electrical signalwith a given frequency. RTC ICs can vary in accuracy up to 100 parts permillion (PPM) and RTC ICs having a higher accuracy tend to cost more.Because RTC ICs vary in accuracy, e.g., counting time at slightlydifferent rates, even when initially set accurate, RTCs will differafter some time due to clock drift.

According to one aspect, a time generation system receives, at a sensorunit, a global positioning system (GPS) pulse signal from a GPS sensorof an autonomous driving vehicle (ADV), where the GPS pulse signal is aRF signal transmitted by a satellite to the GPS sensor, where the sensorunit is coupled to a plurality of sensors mounted on the ADV and a hostsystem, where the host system includes a perception module and aplanning and control (PNC) module, where the perception module is toperceive a driving environment surrounding the ADV based on sensor dataobtained from the sensors and processed by a processing module of thesensor unit, and where the PNC module is to plan a path to autonomouslydrive the ADV based on perception data. The system receives a firstlocal oscillator signal from a local oscillator of the sensor unit. Thesystem synchronizes the first local oscillator signal to the GPS pulsesignal in real-time, including modifying the first local oscillatorsignal based on the GPS pulse signal. The system generates a secondoscillator signal based on the synchronized first local oscillatorsignal, where the second oscillator signal is used to provide a time toat least one of the sensors.

In one embodiment, the system generates a first counter having a firstgranularity using the local oscillator. The system monitors the firstcounter to count a number of oscillations at the first granularity toreach a time interval of the GPS pulse signal, where each countrepresents an oscillation at the first granularity. The systemcalculates a first count value based on the monitored first counter atthe first granularity. The system modifies the first counter so thateach count represents a fraction of an oscillation at the firstgranularity based on the calculated first count value to synchronize thelocal oscillator at the first granularity.

In another embodiment, the system generates a second counter having asecond granularity using the local oscillator. The system monitors thesecond counter to count a number of oscillations at the secondgranularity to reach a time interval of the GPS pulse signal, where eachcount represents an oscillation at the second granularity. The systemcalculates a second count value based on the monitored second counter atthe second granularity. The system modifies the second counter so thateach count represents a fraction of an oscillation at the secondgranularity based on the calculated second count value to synchronizethe local oscillator at the second granularity.

In another embodiment, the system generates a third counter having athird granularity using the local oscillator. The system monitors thethird counter to count a number of oscillations at the third granularityto reach a time interval of the GPS pulse signal, where each countrepresents an oscillation at the third granularity. The systemcalculates a third count value based on the monitored third counter atthe third granularity. The system modifies the third counter so thateach count represents a fraction of an oscillation at the thirdgranularity based on the calculated third count value to synchronize thelocal oscillator at the third granularity.

In another embodiment, the first granularity is a millisecondgranularity, the second granularity is a microsecond granularity, andthe third granularity is a nanosecond granularity. In anotherembodiment, the system disables the generation of any of the first,second, or the third counters for synchronization. In anotherembodiment, the system maintains the first, second, and third countvalues if the GPS sensor signal is lost, until the GPS sensor signal isagain regained.

According to a second aspect, a sensor unit is to be utilized in an ADV.The sensor unit includes a sensor interface to be coupled to a number ofsensors mounted on a number of locations of the ADV. The sensor unitincludes a host interface to be coupled to a host system, where the hostsystem is configured to perceive a driving environment surrounding theADV based on sensor data obtained from the sensors and to plan a path toautonomously drive the ADV. The sensor unit includes a timesynchronization hub device coupled to the sensor interface. The timesynchronization hub device includes one or more transmit (TX) timestampgenerators coupled to a time source, where the TX timestamp generatorsgenerate TX timestamps based on a time obtained from the time source toprovide the TX timestamps to one or more of the sensors indicating atime the sensors transmit sensor data to the host system via the hostinterface. The time synchronization hub device includes one or morereceive (RX) timestamp generators coupled to the time source, where theRX timestamp generators generate RX timestamps based on the timeobtained from the time source to provide the RX timestamps to the one ormore of the sensors indicating a time when sensor data is received fromthe sensors.

In one embodiment, a first of the sensors (e.g., a sensor that acceptstime information input and outputs sensor data with time information) iscoupled to both a TX timestamp generator and a RX timestamp generator,where the sensor is to receive a TX timestamp from the TX timestampgenerator and transmits sensor data and a metadata to the circuit, wherethe metadata includes the TX timestamp information, where the RXtimestamp generator is to generate a RX timestamp to append to thetransmitted metadata to indicate a time when the sensor data is receivedby the circuit.

In one embodiment, a second of the sensors (e.g., a sensor that may ormay not accept time information inputs and outputs only sensor data) iscoupled to an RX timestamp generator but not a TX timestamp generator,where the sensor is to transmit sensor data to the circuit without anytransmit timestamp information, and where the RX timestamp generator isto generate a RX timestamp to append to a metadata of the transmittedsensor data to indicate a time when the sensor data is received by thecircuit.

In one embodiment, a third of the sensors (e.g., a sensor that acceptstime information inputs and outputs directly to a host system) iscoupled to a TX timestamp generator and the host system but not a RXtimestamp generator, where the sensor is to receive a TX timestamp fromthe TX timestamp generator and the third sensor transmits sensor dataand metadata directly to the host system, where the metadata includes TXtimestamp information indicating a time when the sensor data istransmitted to the host system.

In one embodiment, the synchronization hub device is coupled to the hostsystem to synchronize a time of the host system. In another embodiment,the synchronization hub device is coupled to the host system via aperipheral component interconnect express (PCIe) bus.

In one embodiment, a metadata for a camera sensor coupled to a TXtimestamp generator includes camera trigger timestamp information. Inone embodiment, a timestamp format of the TX or RX timestamps includes ams:us:ns:mm:ss:hh:month:day:year or a mm:ss:hh:month:day:year format. Inanother embodiment, the sensor unit includes a format converter unit toconvert a timestamp from one format to another.

According to a third aspect, a system receives a number of times from anumber of time sources including sensors and real-time clocks (RTCs),wherein the sensors are in communication with the ADV and the sensorsinclude at least a GPS sensor, and where the RTCs include at least acentral processing unit real-time clock (CPU-RTC). The system generatinga difference histogram based on a time for each of the time sources fora difference between a time of the GPS sensor and a time for each of theother sensors and RTCs. The system ranks the sensors and RTCs based onthe difference histogram. The system selects a time source from one ofthe sensors or RTCs with a least difference in time with respect to theGPS sensor. The system generates a timestamp based on the selected timesource to timestamp sensor data for a sensor unit of the ADV.

In one embodiment, the difference histogram includes an averagedifference histogram comprising an average time difference distributionfor the GPS sensors and each of the other sensors and RTCs. In oneembodiment, the system defaults to a default list of ranking for sensorsor RTCs when a difference histogram is unavailable.

In one embodiment, the system stores time information of the CPU-RTC toa log file including a time difference of a time for the CPU-RTC and theGPS sensor. The system monitors the time difference of a time for theCPU-RTC and the GPS sensor. The system updates time information in thelog file based on the monitored time difference. In one embodiment, thetime sources include: LTE, WIFI, CPU RTC, FPGA RTC, FM receiver, V2Xsensors, or GPS sensors. In one embodiment, the system logs thedifference histogram and rankings of the sensors and RTCs to a logbuffer.

According to a fourth aspect, a system determines a difference in timebetween a local time source and a time of a GPS sensor. The systemdetermines a max limit in difference and a max recovery increment or maxrecovery time interval for a smooth time source recovery. The systemdetermines that the difference between the local time source and a timeof the GPS sensor to be less than the max limit. The system plans asmooth recovery of the time source to converge (or align) the local timesource to a time of the GPS sensor within the max recovery timeinterval. The system generates a timestamp based on the recovered timesource to timestamp sensor data for a sensor unit of the ADV.

In one embodiment, the system determines that the difference between thelocal time source and a time of the GPS sensor to be greater than themax limit. The system plans an abrupt recovery of the time source toassign the local time source to be a time of the GPS sensor.

In one embodiment, the system plans the smooth recovery of the timesource to converge the local time source to a time of the GPS sensorbased on a predetermined or set time increment different from the maxrecovery increment. In one embodiment, the predetermined time incrementis a single clock cycle interval. In one embodiment, the single clockcycle interval is 10 nanoseconds.

In one embodiment, the max limit and the recovery increment or maxrecovery time interval are preconfigured by a user. In one embodiment,the difference in time between a local time source and a time of the GPSsensor is determined upon detecting a signal recovery from the GPSsensor.

FIG. 1 is a block diagram illustrating an autonomous vehicle networkconfiguration according to one embodiment of the disclosure. Referringto FIG. 1, network configuration 100 includes autonomous vehicle 101that may be communicatively coupled to one or more servers 103-104 overa network 102. Although there is one autonomous vehicle shown, multipleautonomous vehicles can be coupled to each other and/or coupled toservers 103-104 over network 102. Network 102 may be any type ofnetworks such as a local area network (LAN), a wide area network (WAN)such as the Internet, a cellular network, a satellite network, or acombination thereof, wired or wireless. Server(s) 103-104 may be anykind of servers or a cluster of servers, such as Web or cloud servers,application servers, backend servers, or a combination thereof. Servers103-104 may be data analytics servers, content servers, trafficinformation servers, map and point of interest (MPOI) severs, orlocation servers, etc.

An autonomous vehicle refers to a vehicle that can be configured to inan autonomous mode in which the vehicle navigates through an environmentwith little or no input from a driver. Such an autonomous vehicle caninclude a sensor system having one or more sensors that are configuredto detect information about the environment in which the vehicleoperates. The vehicle and its associated controller(s) use the detectedinformation to navigate through the environment. Autonomous vehicle 101can operate in a manual mode, a full autonomous mode, or a partialautonomous mode.

In one embodiment, autonomous vehicle 101 includes, but is not limitedto, perception and planning system 110, vehicle control system 111,wireless communication system 112, user interface system 113,infotainment system 114, and sensor system 115. Autonomous vehicle 101may further include certain common components included in ordinaryvehicles, such as, an engine, wheels, steering wheel, transmission,etc., which may be controlled by vehicle control system 111 and/orperception and planning system 110 using a variety of communicationsignals and/or commands, such as, for example, acceleration signals orcommands, deceleration signals or commands, steering signals orcommands, braking signals or commands, etc.

Components 110-115 may be communicatively coupled to each other via aninterconnect, a bus, a network, or a combination thereof. For example,components 110-115 may be communicatively coupled to each other via acontroller area network (CAN) bus. A CAN bus is a vehicle bus standarddesigned to allow microcontrollers and devices to communicate with eachother in applications without a host computer. It is a message-basedprotocol, designed originally for multiplex electrical wiring withinautomobiles, but is also used in many other contexts.

Referring now to FIG. 2, in one embodiment, sensor system 115 includes,but it is not limited to, one or more cameras 211, global positioningsystem (GPS) unit 212, inertial measurement unit (IMU) 213, radar unit214, and a light detection and range (LIDAR) unit 215. GPS system 212may include a transceiver operable to provide information regarding theposition of the autonomous vehicle. IMU unit 213 may sense position andorientation changes of the autonomous vehicle based on inertialacceleration. Radar unit 214 may represent a system that utilizes radiosignals to sense objects within the local environment of the autonomousvehicle. In some embodiments, in addition to sensing objects, radar unit214 may additionally sense the speed and/or heading of the objects.LIDAR unit 215 may sense objects in the environment in which theautonomous vehicle is located using lasers. LIDAR unit 215 could includeone or more laser sources, a laser scanner, and one or more detectors,among other system components. Cameras 211 may include one or moredevices to capture images of the environment surrounding the autonomousvehicle. Cameras 211 may be still cameras and/or video cameras. A cameramay be mechanically movable, for example, by mounting the camera on arotating and/or tilting a platform.

Sensor system 115 may further include other sensors, such as, a sonarsensor, an infrared sensor, a steering sensor, a throttle sensor, abraking sensor, and an audio sensor (e.g., microphone). An audio sensormay be configured to capture sound from the environment surrounding theautonomous vehicle. A steering sensor may be configured to sense thesteering angle of a steering wheel, wheels of the vehicle, or acombination thereof. A throttle sensor and a braking sensor sense thethrottle position and braking position of the vehicle, respectively. Insome situations, a throttle sensor and a braking sensor may beintegrated as an integrated throttle/braking sensor.

In one embodiment, vehicle control system 111 includes, but is notlimited to, steering unit 201, throttle unit 202 (also referred to as anacceleration unit), and braking unit 203. Steering unit 201 is to adjustthe direction or heading of the vehicle. Throttle unit 202 is to controlthe speed of the motor or engine that in turn controls the speed andacceleration of the vehicle. Braking unit 203 is to decelerate thevehicle by providing friction to slow the wheels or tires of thevehicle. Note that the components as shown in FIG. 2 may be implementedin hardware, software, or a combination thereof.

Referring back to FIG. 1, wireless communication system 112 is to allowcommunication between autonomous vehicle 101 and external systems, suchas devices, sensors, other vehicles, etc. For example, wirelesscommunication system 112 can wirelessly communicate with one or moredevices directly or via a communication network, such as servers 103-104over network 102. Wireless communication system 112 can use any cellularcommunication network or a wireless local area network (WLAN), e.g.,using WiFi to communicate with another component or system. Wirelesscommunication system 112 could communicate directly with a device (e.g.,a mobile device of a passenger, a display device, a speaker withinvehicle 101), for example, using an infrared link, Bluetooth, etc. Userinterface system 113 may be part of peripheral devices implementedwithin vehicle 101 including, for example, a keyboard, a touch screendisplay device, a microphone, and a speaker, etc.

Some or all of the functions of autonomous vehicle 101 may be controlledor managed by perception and planning system 110, especially whenoperating in an autonomous driving mode. Perception and planning system110 includes the necessary hardware (e.g., processor(s), memory,storage) and software (e.g., operating system, planning and routingprograms) to receive information from sensor system 115, control system111, wireless communication system 112, and/or user interface system113, process the received information, plan a route or path from astarting point to a destination point, and then drive vehicle 101 basedon the planning and control information. Alternatively, perception andplanning system 110 may be integrated with vehicle control system 111.

For example, a user as a passenger may specify a starting location and adestination of a trip, for example, via a user interface. Perception andplanning system 110 obtains the trip related data. For example,perception and planning system 110 may obtain location and routeinformation from an MPOI server, which may be a part of servers 103-104.The location server provides location services and the MPOI serverprovides map services and the POIs of certain locations. Alternatively,such location and MPOI information may be cached locally in a persistentstorage device of perception and planning system 110.

While autonomous vehicle 101 is moving along the route, perception andplanning system 110 may also obtain real-time traffic information from atraffic information system or server (TIS). Note that servers 103-104may be operated by a third party entity. Alternatively, thefunctionalities of servers 103-104 may be integrated with perception andplanning system 110. Based on the real-time traffic information, MPOIinformation, and location information, as well as real-time localenvironment data detected or sensed by sensor system 115 (e.g.,obstacles, objects, nearby vehicles), perception and planning system 110can plan an optimal route and drive vehicle 101, for example, viacontrol system 111, according to the planned route to reach thespecified destination safely and efficiently.

Server 103 may be a data analytics system to perform data analyticsservices for a variety of clients. In one embodiment, data analyticssystem 103 includes data collector 121 and machine learning engine 122.Data collector 121 collects driving statistics 123 from a variety ofvehicles, either autonomous vehicles or regular vehicles driven by humandrivers. Driving statistics 123 include information indicating thedriving commands (e.g., throttle, brake, steering commands) issued andresponses of the vehicles (e.g., speeds, accelerations, decelerations,directions) captured by sensors of the vehicles at different points intime. Driving statistics 123 may further include information describingthe driving environments at different points in time, such as, forexample, routes (including starting and destination locations), MPOIs,road conditions, weather conditions, etc.

Based on driving statistics 123, machine learning engine 122 generatesor trains a set of rules, algorithms, and/or predictive models 124 for avariety of purposes. In one embodiment, algorithms 124 may include rulesor algorithms for perception, prediction, decision, planning, and/orcontrol processes, which will be described in details further below.Algorithms 124 can then be uploaded on ADVs to be utilized duringautonomous driving in real-time.

FIGS. 3A and 3B are block diagrams illustrating an example of aperception and planning system used with an autonomous vehicle accordingto one embodiment. System 300 may be implemented as a part of autonomousvehicle 101 of FIG. 1 including, but is not limited to, perception andplanning system 110, control system 111, and sensor system 115.Referring to FIGS. 3A-3B, perception and planning system 110 includes,but is not limited to, localization module 301, perception module 302,prediction module 303, decision module 304, planning module 305, controlmodule 306, and routing module 307.

Some or all of modules 301-307 may be implemented in software, hardware,or a combination thereof. For example, these modules may be installed inpersistent storage device 352, loaded into memory 351, and executed byone or more processors (not shown). Note that some or all of thesemodules may be communicatively coupled to or integrated with some or allmodules of vehicle control system 111 of FIG. 2. Some of modules 301-307may be integrated together as an integrated module.

Localization module 301 determines a current location of autonomousvehicle 300 (e.g., leveraging GPS unit 212) and manages any data relatedto a trip or route of a user. Localization module 301 (also referred toas a map and route module) manages any data related to a trip or routeof a user. A user may log in and specify a starting location and adestination of a trip, for example, via a user interface. Localizationmodule 301 communicates with other components of autonomous vehicle 300,such as map and route information 311, to obtain the trip related data.For example, localization module 301 may obtain location and routeinformation from a location server and a map and POI (MPOI) server. Alocation server provides location services and an MPOI server providesmap services and the POIs of certain locations, which may be cached aspart of map and route information 311. While autonomous vehicle 300 ismoving along the route, localization module 301 may also obtainreal-time traffic information from a traffic information system orserver.

Based on the sensor data provided by sensor system 115 and localizationinformation obtained by localization module 301, a perception of thesurrounding environment is determined by perception module 302. Theperception information may represent what an ordinary driver wouldperceive surrounding a vehicle in which the driver is driving. Theperception can include the lane configuration, traffic light signals, arelative position of another vehicle, a pedestrian, a building,crosswalk, or other traffic related signs (e.g., stop signs, yieldsigns), etc., for example, in a form of an object. The laneconfiguration includes information describing a lane or lanes, such as,for example, a shape of the lane (e.g., straight or curvature), a widthof the lane, how many lanes in a road, one-way or two-way lane, mergingor splitting lanes, exiting lane, etc.

Perception module 302 may include a computer vision system orfunctionalities of a computer vision system to process and analyzeimages captured by one or more cameras in order to identify objectsand/or features in the environment of autonomous vehicle. The objectscan include traffic signals, road way boundaries, other vehicles,pedestrians, and/or obstacles, etc. The computer vision system may usean object recognition algorithm, video tracking, and other computervision techniques. In some embodiments, the computer vision system canmap an environment, track objects, and estimate the speed of objects,etc. Perception module 302 can also detect objects based on othersensors data provided by other sensors such as a radar and/or LIDAR.

For each of the objects, prediction module 303 predicts what the objectwill behave under the circumstances. The prediction is performed basedon the perception data perceiving the driving environment at the pointin time in view of a set of map/rout information 311 and traffic rules312. For example, if the object is a vehicle at an opposing directionand the current driving environment includes an intersection, predictionmodule 303 will predict whether the vehicle will likely move straightforward or make a turn. If the perception data indicates that theintersection has no traffic light, prediction module 303 may predictthat the vehicle may have to fully stop prior to enter the intersection.If the perception data indicates that the vehicle is currently at aleft-turn only lane or a right-turn only lane, prediction module 303 maypredict that the vehicle will more likely make a left turn or right turnrespectively.

For each of the objects, decision module 304 makes a decision regardinghow to handle the object. For example, for a particular object (e.g.,another vehicle in a crossing route) as well as its metadata describingthe object (e.g., a speed, direction, turning angle), decision module304 decides how to encounter the object (e.g., overtake, yield, stop,pass). Decision module 304 may make such decisions according to a set ofrules such as traffic rules or driving rules 312, which may be stored inpersistent storage device 352.

Routing module 307 is configured to provide one or more routes or pathsfrom a starting point to a destination point. For a given trip from astart location to a destination location, for example, received from auser, routing module 307 obtains route and map information 311 anddetermines all possible routes or paths from the starting location toreach the destination location. Routing module 307 may generate areference line in a form of a topographic map for each of the routes itdetermines from the starting location to reach the destination location.A reference line refers to an ideal route or path without anyinterference from others such as other vehicles, obstacles, or trafficcondition. That is, if there is no other vehicle, pedestrians, orobstacles on the road, an ADV should exactly or closely follows thereference line. The topographic maps are then provided to decisionmodule 304 and/or planning module 305. Decision module 304 and/orplanning module 305 examine all of the possible routes to select andmodify one of the most optimal routes in view of other data provided byother modules such as traffic conditions from localization module 301,driving environment perceived by perception module 302, and trafficcondition predicted by prediction module 303. The actual path or routefor controlling the ADV may be close to or different from the referenceline provided by routing module 307 dependent upon the specific drivingenvironment at the point in time.

Based on a decision for each of the objects perceived, planning module305 plans a path or route for the autonomous vehicle, as well as drivingparameters (e.g., distance, speed, and/or turning angle), using areference line provided by routing module 307 as a basis. That is, for agiven object, decision module 304 decides what to do with the object,while planning module 305 determines how to do it. For example, for agiven object, decision module 304 may decide to pass the object, whileplanning module 305 may determine whether to pass on the left side orright side of the object. Planning and control data is generated byplanning module 305 including information describing how vehicle 300would move in a next moving cycle (e.g., next route/path segment). Forexample, the planning and control data may instruct vehicle 300 to move10 meters at a speed of 30 mile per hour (mph), then change to a rightlane at the speed of 25 mph.

Based on the planning and control data, control module 306 controls anddrives the autonomous vehicle, by sending proper commands or signals tovehicle control system 111, according to a route or path defined by theplanning and control data. The planning and control data includesufficient information to drive the vehicle from a first point to asecond point of a route or path using appropriate vehicle settings ordriving parameters (e.g., throttle, braking, steering commands) atdifferent points in time along the path or route.

In one embodiment, the planning phase is performed in a number ofplanning cycles, also referred to as driving cycles, such as, forexample, in every time interval of 100 milliseconds (ms). For each ofthe planning cycles or driving cycles, one or more control commands willbe issued based on the planning and control data. That is, for every 100ms, planning module 305 plans a next route segment or path segment, forexample, including a target position and the time required for the ADVto reach the target position. Alternatively, planning module 305 mayfurther specify the specific speed, direction, and/or steering angle,etc. In one embodiment, planning module 305 plans a route segment orpath segment for the next predetermined period of time such as 5seconds. For each planning cycle, planning module 305 plans a targetposition for the current cycle (e.g., next 5 seconds) based on a targetposition planned in a previous cycle. Control module 306 then generatesone or more control commands (e.g., throttle, brake, steering controlcommands) based on the planning and control data of the current cycle.

Note that decision module 304 and planning module 305 may be integratedas an integrated module. Decision module 304/planning module 305 mayinclude a navigation system or functionalities of a navigation system todetermine a driving path for the autonomous vehicle. For example, thenavigation system may determine a series of speeds and directionalheadings to affect movement of the autonomous vehicle along a path thatsubstantially avoids perceived obstacles while generally advancing theautonomous vehicle along a roadway-based path leading to an ultimatedestination. The destination may be set according to user inputs viauser interface system 113. The navigation system may update the drivingpath dynamically while the autonomous vehicle is in operation. Thenavigation system can incorporate data from a GPS system and one or moremaps so as to determine the driving path for the autonomous vehicle.

FIG. 4 is a block diagram illustrating system architecture forautonomous driving according to one embodiment. System architecture 400may represent system architecture of an autonomous driving system asshown in FIGS. 3A and 3B. Referring to FIG. 4, system architecture 400includes, but it is not limited to, application layer 401, planning andcontrol (PNC) layer 402, perception layer 403, driver layer 404,firmware layer 405, and hardware layer 406. Application layer 401 mayinclude user interface or configuration application that interacts withusers or passengers of an autonomous driving vehicle, such as, forexample, functionalities associated with user interface system 113. PNClayer 402 may include functionalities of at least planning module 305and control module 306. Perception layer 403 may include functionalitiesof at least perception module 302. In one embodiment, there is anadditional layer including the functionalities of prediction module 303and/or decision module 304. Alternatively, such functionalities may beincluded in PNC layer 402 and/or perception layer 403. Systemarchitecture 400 further includes driver layer 404, firmware layer 405,and hardware layer 406. Firmware layer 405 may represent at least thefunctionality of sensor system 115, which may be implemented in a formof a field programmable gate array (FPGA). Hardware layer 406 mayrepresent the hardware of the autonomous driving vehicle such as controlsystem 111 and/or sensor system 115. Layers 401-403 can communicate withfirmware layer 405 and hardware layer 406 via device driver layer 404.

FIG. 5 is a block diagram illustrating an example of a sensor systemaccording to one embodiment of the invention. Referring to FIG. 5,sensor system 115 includes a number of sensors 510 and a sensor unit 500coupled to host system 110. Host system 110 represents a planning andcontrol system as described above, which may include at least some ofthe modules as shown in FIGS. 3A and 3B. Sensor unit 500 may beimplemented in a form of an FPGA device or an ASIC (application specificintegrated circuit) device. In one embodiment, sensor unit 500 includes,amongst others, one or more sensor data processing modules 501 (alsosimply referred to as sensor processing modules), data transfer modules502, and sensor control modules or logic 503. Modules 501-503 cancommunicate with sensors 510 via a sensor interface 504 and communicatewith host system 110 via host interface 505. Optionally, an internal orexternal buffer 506 may be utilized for buffering the data forprocessing.

In one embodiment, for the receiving path or upstream direction, sensorprocessing module 501 is configured to receive sensor data from a sensorvia sensor interface 504 and process the sensor data (e.g., formatconversion, error checking), which may be temporarily stored in buffer506. Data transfer module 502 is configured to transfer the processeddata to host system 110 using a communication protocol compatible withhost interface 505. Similarly, for the transmitting path or downstreamdirection, data transfer module 502 is configured to receive data orcommands from host system 110. The data is then processed by sensorprocessing module 501 to a format that is compatible with thecorresponding sensor. The processed data is then transmitted to thesensor.

In one embodiment, sensor control module or logic 503 is configured tocontrol certain operations of sensors 510, such as, for example, timingof activation of capturing sensor data, in response to commands receivedfrom host system (e.g., perception module 302) via host interface 505.Host system 110 can configure sensors 510 to capture sensor data in acollaborative and/or synchronized manner, such that the sensor data canbe utilized to perceive a driving environment surrounding the vehicle atany point in time.

Sensor interface 504 can include one or more of Ethernet, USB (universalserial bus), LTE (long term evolution) or cellular, WiFi, GPS, camera,CAN, serial (e.g., universal asynchronous receiver transmitter or UART),SIM (subscriber identification module) card, and other general purposeinput/output (GPIO) interfaces. Host interface 505 may be any high speedor high bandwidth interface such as PCIe. Sensors 510 can include avariety of sensors that are utilized in an autonomous driving vehicle,such as, for example, a camera, a LIDAR device, a RADAR device, a GPSreceiver, an IMU, an ultrasonic sensor, a GNSS (global navigationsatellite system) receiver, an LTE or cellular SIM card, vehicle sensors(e.g., throttle, brake, steering sensors), and system sensors (e.g.,temperature, humidity, pressure sensors), etc.

For example, a camera can be coupled via an Ethernet or a GPIOinterface. A GPS sensor can be coupled via a USB or a specific GPSinterface. Vehicle sensors can be coupled via a CAN interface. A RADARsensor or an ultrasonic sensor can be coupled via a GPIO interface. ALIDAR device can be coupled via an Ethernet interface. An external SIMmodule can be coupled via an LTE interface. Similarly, an internal SIMmodule can be inserted onto a SIM socket of sensor unit 500. The serialinterface such as UART can be coupled with a console system for debugpurposes.

Note that sensors 510 can be any kind of sensors and provided by variousvendors or suppliers. Sensor processing module 501 is configured tohandle different types of sensors and their respective data formats andcommunication protocols. According to one embodiment, each of sensors510 is associated with a specific channel for processing sensor data andtransferring the processed sensor data between host system 110 and thecorresponding sensor. Each channel includes a specific sensor processingmodule and a specific data transfer module that have been configured orprogrammed to handle the corresponding sensor data and protocol.

In one embodiment, sensor unit 500 includes high precision timegeneration circuit 517. High precision time generation circuit 517 cangenerate a time and/or a timestamp to be used by each of sensors 510 tokeep track of when sensor data are transmitted or captured/triggered byeach of sensors 510, and/or received by sensor unit 500, as shown inFIGS. 6A-6B.

Referring now to FIG. 6A, high precision time generation circuit 517 caninclude time synchronization unit 550, GPS sensor 551, and local timer553. Time synchronization unit 550 can synchronize local timer 553 withrespect to a time derived from a pulse per second (PPS) signal from GPSsensor 551. The PPS can be used to align local timer 553 for precisetime measurements, to the nanoseconds. GPS sensor 551 can be part of GPSunit 212 of sensor system 115 of FIG. 2 or GPS sensor 551 can be adedicated GPS sensor integrated within high precision time generationcircuit 517. Local timer 553 can generate a time for sensor unit 500.Local timer 553 can be a timer from any local RTCs (e.g., CPU RTC orFPGA RTC) or sensors of sensor unit 500, or a time retrieved from anexternal source such as a cellular source, e.g., 4G, long-term evolution(LTE), 5G, a WIFI source, FM receiver, etc.

Referring to FIG. 6B, time synchronization unit 550 can include monitormodule 555, adjust module 557, millisecond generator 603, microsecondgenerator 605, nanosecond generator 607, de-multiplexer 609, andconfiguration 611. Millisecond generator 603, microsecond generator 605,and nanosecond generator 607 can generate millisecond, microsecond, andnanosecond oscillation cycles respectively (e.g., oscillator counters atthree different granularities) based on an oscillator of local timer553. Configuration 611 can configure a select signal to select which ofthe outputs for millisecond generator 603, microsecond generator 605,and nanosecond 607 are to be routed to monitor module 555. Monitormodule 555 can monitor the generated oscillation cycles to count thesecycles. Adjust module 557 can adjust the counts (or modifies the countrepresentations) so to sync the local timer 553 with a PPS signal fromGPS sensor 551. In one embodiment, select signal for configuration 611can be programmed by a user of sensor unit 500 or by monitor module555/adjust module 557 in a feedback loop. For example, a user canconfigure to disable the millisecond generator if it is determined thatlocal timer 553 is relatively precise.

Depending on the type of crystal oscillators used, local timer 553 canhave an accuracy ranging from 0.1 to 100 ppm, e.g., any pulse can be offby 0.1 to 100 microseconds, whereas the pulse per second (PPS) signalfrom GPS sensor 551 has an accuracy rate of less than 0.1 ppm, or lessthan 0.1 microseconds of deviations for each pulse for each second. Fora 0.1 ppm GPS PPS signal, a received PPS signal from GPS sensor 551 canassert consecutive pulses to be between 999,999.9 and 1,000,000.1microseconds every second, while a typical 100 ppm local timer 553 canassert consecutive pulses to be between 999,900 and 1,000,100microseconds every second. Furthermore, the variations in deviations ofthe pulses for local timer 553 can change in real-time due to changes inambient temperature of the crystal oscillator ICs using by local timer553. Thus, an objective is to adjust or sync local timer 553 to matchGPS sensor 551 in real-time.

To sync local timer 553 to GPS sensor 551, in one embodiment, GPS sensor551 receives a GPS pulse signal (PPS) that is a RF signal transmitted bya satellite broadcasting its signal in space with a certain accuracyrate, e.g., <0.1 ppm. In some embodiments, GPS sensor 551 receives thePPS signal from a first GPS satellite followed by a PPS signal from asecond GPS satellite if the first GPS satellite is out of range. BecauseGPS satellites use its own precise measure of time with each satellitehaving its own on-board set of atomic clocks, PPS signals from the GPSsatellites can be viewed as one or more reference timers. Note, however,because local timer 553 is adjusted in real-time to match any one GPSPPS signal, it is assumed that any time discrepancies when GPS PPSsignals of two or more different GPS satellites are not a concern sincethe local timer 553 can be synced smoothly in real-time, as describedfurther below.

Once a GPS PPS signal is received, monitor module 555 can determine anyoffsets of a time of the PPS signal and a time for local timer 553 andcan generate a second local real-time clock/timer based on thedetermined offsets. For example, based on the PPS signal, date and timeinformation (Coordinated Universal Time or UTC format) can initially beprovided by GPS (National Marine Electronics Association) NMEA datainformation, accurate up to the seconds. Next, in one embodiment,milliseconds generator 603 can generate a close-to-one-millisecondoscillation count (e.g., a first granularity) using local timer 553. Theclose-to-one-millisecond oscillation count can be generated using afrequency divider circuit to divide a signal frequency of the localtimer 553. Monitor module 555 may then detect or count a number ofcycles (e.g., 999 cycles) from milliseconds generator 603 for a GPS PPSsignal time interval of one second, e.g., local timer 553 lags the GPSPPS signal by about one millisecond. Because milliseconds generator 603lags the GPS PPS, in one embodiment, adjust module 557 adjusts themilliseconds generator output to represent 1.001 milliseconds peroscillation. Milliseconds generator 603 then generates the following1000 oscillation representations for each second: 0.000, 1.001, 2.002, .. . , 999.999, and 1001 milliseconds. So the 999^(th) cycle frommilliseconds generator 603 counts to 999.999 milliseconds.

Next, microseconds generator 605 can generate a close-to-one-microsecondoscillation count using local timer 553. The close-to-one-microsecondoscillation count (e.g., a second granularity) can be generated using asecond frequency divider circuit to divide a signal frequency of thelocal timer 553. Monitor module 555 may count 998 cycles frommicroseconds generator 605 or a 2 microseconds offset for a GPS PPS timeinterval of one millisecond. Again, because microseconds generator 605lags the GPS PPS, adjust module 557 adjusts the microseconds generatoroutput to represent 1.002 microseconds per oscillation. The microsecondsgenerator then generates the following 1000 oscillation representationsfor each millisecond: 0.000, 1.002, 2.004, . . . , 999.996, 1000.998,and 1002 microseconds. So the 998^(th) cycle counts to 999.996microseconds.

Next, nanoseconds generator 607 can generate a close-to-one-nanosecondoscillation count using local timer 553. The close-to-one-nanosecondoscillation count (e.g., a third granularity) can be generated using athird frequency divider circuit to divide a signal frequency of thelocal timer 553. Monitor module 555 may count 997 cycles fromnanoseconds generator 607 or detect a 3 nanoseconds offset for a GPS PPSsignal time interval of one microsecond. Again, adjust module 557 canadjust the nanoseconds generator output to represent 1.003 nanosecondper oscillation. The nanoseconds generator then generates the following1000 oscillation representations for each microsecond: 0.000, 1.003,2.006, . . . , 999.991, 1000.994, 1001.997, and 1003 nanoseconds. So the997^(th) cycle from the nanoseconds generator 607 counts to 999.991nanoseconds. This way, any of the generator outputs (e.g.,representations) or a combination thereof, can generate a high precisiontime in real-time. The high precision time can then be provided to thesensors of sensor unit 500. In the above example, the generated time hasa precision up to one nanosecond using the nanoseconds generator. Note,although three generators (e.g., three granularities) are described, anynumber of generators and granularities can be used to generate a highprecision time.

In some embodiment, configuration 611 can selectively enable/disable,via de-multiplexer 609, any of generators 603-607. The selectivity canturn on/off any of the generators. Selectivity is useful to select asubset of the generator outputs (e.g., only nanosecond generator) whenonly a subset of the outputs is required. In another embodiment, monitormodule 555 buffers (e.g., saves) the offsets for the differentgranularities and maintaining the first, second, and third count values(e.g., value representations per oscillation) if a GPS sensor signal islost, until the GPS sensor signal is again regained.

FIG. 7 is a flow diagram illustrating a method according to oneembodiment. Processing 700 may be performed by processing logic whichmay include software, hardware, or a combination thereof. For example,process 700 may be performed by sensor unit 500 of FIG. 5. Referring toFIG. 7, at block 701, processing logic receives, at a sensor unit, aglobal positioning system (GPS) pulse signal from a GPS sensor of theADV, where the GPS pulse signal is a RF signal transmitted by asatellite to the GPS sensor, where the sensor unit is coupled to aplurality of sensors mounted on the ADV and a host system, where thehost system includes a perception module and a planning and control(PNC) module, where the perception module is to perceive a drivingenvironment surrounding the ADV based on sensor data obtained from thesensors and processed by a processing module of the sensor unit, andwhere the PNC module is to plan a path to autonomously drive the ADVbased on perception data. At block 702, processing logic receives afirst local oscillator signal from a local oscillator of the sensorunit. At block 703, processing logic synchronizes the first localoscillator signal to the GPS pulse signal in real-time, includingmodifying the first local oscillator signal based on the GPS pulsesignal. At block 704, processing logic generates a second oscillatorsignal based on the synchronized first local oscillator signal, whereinthe second oscillator signal is provided to at least one of the sensorsto be used as a clock signal to operate the sensor unit.

In one embodiment, synchronizing the local oscillator includesgenerating a first counter having a first granularity using the localoscillator, monitoring the first counter to count a number ofoscillations at the first granularity to reach a time interval of theGPS pulse signal, where each count represents an oscillation at thefirst granularity, calculating a first count value based on themonitored first counter at the first granularity, and modifying thefirst counter so that each count represents a fraction of an oscillationat the first granularity based on the calculated first count value tosynchronize the local oscillator at the first granularity.

In another embodiment, synchronizing the local oscillator includesgenerating a second counter having a second granularity using the localoscillator, monitoring the second counter to count a number ofoscillations at the second granularity to reach a time interval of theGPS pulse signal, where each count represents an oscillation at thesecond granularity, calculating a second count value based on themonitored second counter at the second granularity, and modifying thesecond counter so that each count represents a fraction of anoscillation at the second granularity based on the calculated secondcount value to synchronize the local oscillator at the secondgranularity.

In another embodiment, synchronizing the local oscillator includesgenerating a third counter having a third granularity using the localoscillator, monitoring the third counter to count a number ofoscillations at the third granularity to reach a time interval of theGPS pulse signal, where each count represents an oscillation at thethird granularity, calculating a third count value based on themonitored third counter at the third granularity, and modifying thethird counter so that each count represents a fraction of an oscillationat the third granularity based on the calculated third count value tosynchronize the local oscillator at the third granularity. In anotherembodiment, the first granularity is a millisecond granularity, thesecond granularity is a microsecond granularity, and the thirdgranularity is a nanosecond granularity.

In another embodiment, processing logic further disables the generationof any of the first, second, or the third counters for synchronization.In another embodiment, processing logic further maintains the first,second, and third count values if the GPS sensor signal is lost, untilthe GPS sensor signal is again regained.

FIG. 8 is a block diagram illustrating an example of a sensor unitaccording to one embodiment. FIG. 8 is similar to FIG. 5 except with theaddition of timestamp sync hub device 519. Timestamp sync hub device 519can generate one or more timestamps (e.g., receive RX timestamp,transmit TX timestamp, and/or trigger timestamp) for any of sensors 510or simply provide a time information to any of sensors 510. Timestampsync hub device 519 can be coupled to each sensor in various fashions,but each sensor is coupled to a TX timestamp generator, a RX timestampgenerator, or both. Accuracy of RX/TX and trigger timestamps is crucialto keep track of sensor data acquisition times.

FIG. 9 is a block diagram illustrating an example of a timestamp synchub device according to one embodiment. Timestamp sync hub device 519includes time generation or GPS pulse unit 901, timestamp formatconverters 903, TX timestamp generators 911-913 and RX timestampgenerators 921-923. Timestamp sync hub device 519 is coupled to a numberof sensors (e.g., S1, S2, and S3) to provide TX/RX and/or triggertimestamps for the sensors. Time generation or GPS pulse unit 901 cangenerate a time or provide a GPS pulse for the sensors S1-S3. Timestampformat converter 903 can convert one timestamp format to another, forexample, a timestamp may be converted from a format ofms:us:ns:mm:ss:hh:month:day:year to a format mm:ss:hh:month:day:year.The timestamp format can include year, month, day, hour, minutes,seconds, milliseconds, microseconds, and nanoseconds in any combinationand/or ordering. Timestamp format converter 903 can thus convert oneformat to another as required by time input parameters of some sensors,such as sensors S1 and S3.

TX timestamp generators 911-913 can generate a transmit timestamp forsensors of the sensor unit. In one embodiment, TX timestamp generatorcan simply route the GPS PPS to one or more sensors to provide a GPS PPSsignal to the sensors (e.g., S1). Examples of a S1 sensor includeVelodyne's LIDAR sensors which accept a GPS time information as aninput. The GPS time input information is used to sync the LIDAR sensorto a GPS clock. After the sensor is synced, the LIDAR sensors cantrigger/capture a depth image and include a trigger timestamp with thedepth image. A second timestamp may be a transmit timestamp whichrepresent a time when sensor S1 transmits sensor data from S1 to sensorunit 500. Here, the trigger timestamp and/or the transmit timestamp maybe sent as metadata with the depth image from sensor S1 to sensor unit500.

Another example of a S1 sensor includes a camera sensor which may accepta mm:ss:hh:month:day:year formatted time information as an inputparameter. In this case, TX timestamp generator generates amm:ss:hh:month:day:year format TX timestamp (as provided by timegeneration unit 901) to be sent to the camera sensor. The camera sensorcan trigger/capture a RGB image having a trigger timestamp which can bederived from the TX timestamp (e.g., accounting for any in betweendelays). A second timestamp (transmit timestamp) representing when thesensor data is transmitted to sensor unit may be include with thetrigger timestamp, as time information metadata. The sensor data alongwith the time information metadata can then be transmitted from camerasensor to sensor unit 500. Other examples of S1 sensors include RADARsensors, SONAR sensors, and any sensors that accept a time inputparameter.

In another embodiment, TX timestamp generator generates a timestamp inthe mm:ss:hh:month:day:year format and provide the generated timestampfor one or more sensors, the mm:ss:hh:month:day:year timestamp havingbeen synchronized with a GPS PPS signal. These sensors (e.g., S3) maytransmit sensor data and timestamp metadata (unaltered) directly to hostsystem 110. A direct coupling to host system 110 may be established whenno more communication channels are available or when the sensor dataonly requires a low bandwidth, such as an Ethernet connection. Examplesof S3 sensors can include Ethernet, camera and/or RADAR sensors, etc.

RX timestamp generators 921-923 can generate a receive timestamp at thetime when sensor unit 500 receives the sensor data and to add thegenerated receive timestamp as time metadata to the sensor data. So whenthe sensor data are sent to host system 110, there is availableinformation about a time when sensor unit 500 acquired the sensor data.Examples of sensors that use RX timestamp generators are S1 and S2. Thedifference between S1 and S2 is that S1 also provides transmit (TX)and/or trigger timestamp information, whereas S2 provides only receive(RX) timestamp information. Examples of S2 sensors include LIDAR, cameraand/or RADAR sensors, etc.

In another embodiment, timestamp sync hub device 519 is coupled to hostsystem 110 (e.g., through PCIe bus) to provide a time information (e.g.,time information/timestamps 313) to host system 110. The provided timeinformation allows host system 110 to sync an RTC (e.g., CPU-RTC) ofhost system 110 to the provided time such that a single global time isused among sensor unit 500 and host system 110. Thereafter a planningand control module of host system 110 for the ADV can plan and controlthe ADV autonomously using a local RTC of host system 110 which issynced to sensor unit 500.

FIG. 10 is a block diagram illustrating an example of a sensor unitaccording to one embodiment. FIG. 10 is similar to FIG. 8 except withthe addition of time source ranking module 521. Time source rankingmodule 521 can rank a number of available time source according tosuitability of the time source to be used as a local RTC (e.g., localtimer 553 of FIG. 6A).

FIG. 11 is a block diagram illustrating an example of a time sourceranking circuit according to one embodiment. Referring to FIG. 11, timesource ranking module 521 can include sub-modules such as receivingmodule 1101, histograms generator 1103, histograms ranking module 1105,histograms selection module 1107, timestamp generator 1109, CPU-RTCmonitor module 1111, and logger 1113. Time sources 1120 may include GPSsensor, LTE, CPU RTC, WIFI, FPGA RTC, FM receiver, and V2X sensors.

Receiving module 1101 can receive timestamps or time information fromthe different time sources 1120. Histograms generator 1103 can generatea difference histogram based on the received timestamps or timeinformation from the different time sources 1120. In one embodiment, thedifference histogram can be a difference histogram averaged over aperiod of time, e.g., a few hours or a day. In another embodiment, thedifference histogram averaged over a period of time can be a runningaverage. Histograms ranking module 1105 can rank the time sources usingthe GPS sensor time source as a reference time source, e.g., the timesource is ranked from least to greatest time differences in comparisonto a time of the GPS sensor. Histograms selection module 1107 can selecta time source with a time with the least difference to a time of the GPSsensor time source. Timestamp generator 1109 can generate timeinformation or timestamps based on the selected time source. CPU-RTCmonitor module 1111 can monitor a time different of a time for theCPU-RTC and the GPS sensor. Logger 1113 can store time information ofthe CPU-RTC to a log file such as a delta time or time difference of atime for the CPU-RTC and the GPS sensor.

Time source ranking module 521 is used by an ADV when the ADV ignitionis turned on in an underground parking garage (e.g., when there is noGPS signal). In one embodiment, because sensor unit 500 or host system110 still requires a relatively accurate time to generate timestamps, ifthere lacks information about accuracies of available time sources ofsensor unit 500 or the ADV, a default ranking list of time sources canbe used to determine a preferred time source. An example of a defaultranking list can rank various time sources in an order such as: GPS,FPGA RTC, WIFI, LTE, and CPU RTC. In this case, if the only availabletime sources are from an FPGA RTC and a WIFI signal, the FPGA RTC timesource has priority and is the preferred time source to establish alocal time, such as a local time of local timer 553 of FIG. 6A. However,as soon as a signal from a GPS sensor is available, in one embodiment,time source ranking module 521 determines accuracy of the differentavailable time sources based on a ranking system.

In one embodiment, time source ranking module 521 generates a histogrambased on the absolute difference values of the many available timesources, by a ranking system. The histograms or difference histogramsmay be generated for a few rounds, or these histograms can be averagedover a predetermined period of time, or as long as the GPS signal isavailable. The closest time source (e.g., least difference) to the GPSsensor as determined by the difference histogram is selected as the besttime source to be used when the GPS signal is unavailable, e.g., thenext time the ADV starts ignition in an underground garage. In oneembodiment, a CPU RTC is preconfigured to be the preferred time source.In this case, a difference histogram, a delta time, or an average deltatime for the CPU RTC is written to a log file or a log buffer by logger1113. The difference histogram, a delta time, or an average delta timerecords a time difference for the CPU RTC and the GPS time. At the nextignition, without a GPS signal, sensor unit 500 can then adjusts the CPURTC based on the delta time and uses the adjusted CPU RTC as the localtime. In one embodiment, when the GPS signal is available, the deltatime can be updated to reflect any deviations of the CPU RTC.

FIG. 12 is a flow diagram of a method to rank time sources according toone embodiment. Processing 1200 may be performed by processing logicwhich may include software, hardware, or a combination thereof. Forexample, process 1200 may be performed by time source ranking module 521of FIG. 11. Referring to FIG. 12, at block 1201, processing logicreceives a number of times from a number of time sources includingsensors and RTCs, where the sensors are in communication with the ADVand the sensors include at least a GPS sensor, and where the RTCsinclude at least a central processing unit real-time clock (CPU-RTC).Note that CPU-RTC is a RTC for a CPU of the sensor unit. Examples of RTCand sensor time sources include GPS sensor, LTE, CPU RTC, WIFI, FPGARTC, FM receiver, and V2X sensors. At block 1202, processing logicgenerates a difference histogram based on a time for each of the timesources for a difference between a time of the GPS sensor and a time foreach of the other sensors and RTCs. At block 1203, processing logicranks the sensors and RTCs based on the difference histogram. At block1204, processing logic selects a time source from one of the sensors orRTCs with a least difference in time with respect to the GPS sensor. Atblock 1205, processing logic generates a timestamp based on the selectedtime source to timestamp sensor data for a sensor unit of the ADV.

In one embodiment, the difference histogram includes an averagedifference histogram comprising an average time difference distributionfor the GPS sensors and each of the other sensors and RTCs. In oneembodiment, processing logic defaults to a default list of ranking forsensors or RTCs when a difference histogram is unavailable.

In one embodiment, processing logic further stores time information ofthe CPU-RTC to a log file including a time difference of a time for theCPU-RTC and the GPS sensor, monitors the time difference of a time forthe CPU-RTC and the GPS sensor, and updates time information in the logfile based on the monitored time difference. In another embodiment, thetime information is stored as binary data. In one embodiment, the timeinformation is stored in a log buffer. In one embodiment, the timesources include: LTE, WIFI, CPU RTC, FPGA RTC, FM receiver, V2X sensors,or GPS sensors. In one embodiment, processing logic further logs thedifference histogram and rankings of the sensors and RTCs to a log fileor a log buffer.

FIG. 13 is a block diagram illustrating an example of a sensor unitaccording to one embodiment. FIG. 13 is similar to FIG. 10 except withthe addition of time source recovery module 523. Time source recoverymodule 523 can recover a local time source when a GPS signal isunavailable for a period of time and then became available, e.g.,recovery of a clock drift or a built-up of time discrepancy between thelocal time source and the GPS time source over the period of time.

FIG. 14 is a block diagram illustrating an example of a time sourcerecovery circuit according to one embodiment. Referring to FIG. 14, timesource recovery module 523 includes submodules such as time differencedeterminer module 1401, max limit/step determiner module 1403, smoothrecovery module 1405, abrupt recovery module 1407, and GPS sensorrecovery detector 1409.

Time difference determiner module 1401 can determine a time differenceor time discrepancy between a local time source and a GPS time source.Max limit/step determiner module 1403 can determine a max limit of atime discrepancy where a time recovery is to be performed smoothly,instead of abruptly. For example, if the time discrepancy is below apredetermined max limit then a time recovery is performed according to asmoothing logic. If the time discrepancy is above the max limit then atime recovery is performed according to an abrupt logic, e.g., animmediate alignment of the local time to the GPS time. Max limit/stepdeterminer module 1403 can also determine a recovery increment/step (orrecovery time interval) for a smooth time source recovery according tothe smoothing logic. Smooth recovery module 1405 includes the smoothinglogic and can perform a smooth time source recovery. Recovering smoothlyis when the local time source catches up or aligns with the GPS timeover a predetermined period of time using a predetermined incrementsteps. Abrupt recovery module 1407 can perform an abrupt time sourcerecovery, e.g., align a time of the time source to the GPS time abruptlyor immediately. GPS sensor recovery detector 1409 can detect transitionsof a GPS signal, when it transitions from unavailable to availableand/or when it transitions from available to unavailable.

For illustration, while ADV is on a surface road and can access anavailable GPS signal, local time (or local real-time clock) can besourced and synced by the GPS PPS. When ADV enters a stretch of tunnel,GPS signal may become unavailable, and a local RTC or sensor such as CPURTC can take over the function of a time source. Over the stretch of thetunnel, because the local time source may be inaccuracy, a timediscrepancy (e.g., a time difference between a time of the local timesource and the GPS time) develops due to clock drift. As soon as the ADVexits the tunnel, GPS sensor recovery detector 1409 detects a GPS signaltransition. Time difference determiner module 1401 then determines orcalculates the time discrepancy between the local time source (the localtime source may be selected based on time source difference histograms,as described above) and the GPS time. Max limit/step determiner module1403 then compares the time difference with a max limit (the max limitmay be preconfigured by a user), if the time difference is greater thanthe max limit then a smoothing logic is applied (using a userpreconfigured recovery increment/step) to the local time source to alignthe local time source to the GPS time. Else, an abrupt logic is appliedto the local time source to align the local time source to the GPS time.

FIG. 15 is a time chart illustrating an example of a smooth time sourcerecovery according to one embodiment. Referring to FIG. 15, at time=0,when a GPS signal is detected to transition from unavailable toavailable (e.g., became available), an initial time difference isdetermined to be a lag of 200 nanoseconds (ns). The initial timedifference is compared with a predetermined max limit (e.g., 500 ms) andis determined to be less than the max limit. In this case, because theinitial time difference is determined to be less than the max limit, asmoothing logic is applied. Moreover, because the local time source lagsthe GPS time, the local time source is incrementally increases(according to a preconfigured step increment or a max incrementinterval) to catch up with the GPS time. In one embodiment, theincrement step is predetermined based on a digital clock cycle timeinterval of the digital clock for the sensor unit. For example, theincrement step can be 10 ns because the clock cycle of the sensor unithas a 10 ns time interval. In this case, for each digital clock cycles(e.g., 10 ns), the local time source increments time by the cycleinterval plus the increment, e.g., 20 ns. After 20 digital clock cycles(or 200 ns), the local clock source would then be aligned with the GPStime. Note, the max limit of 500 ms with an increment step of 10 nswould provide a maximum time recovery period of 500 ms. Here, theincrement step can be adjusted to speed up or slow down the smoothrecovery time interval.

If the initial time difference is determined to be greater than 500 msthen the local time source is abruptly aligned to the GPS time withoutany smoothing logic. Note, adjusting the time source does not interferewith the local digital clock for sensor unit but only adjusts thetimestamp clock source that provides the TX/RX and trigger timestampsfor the one or more sensors of the sensor unit. Although 500 ms and 10ns are described as the max limit and recovery increment/steprespectively, any time intervals can be used as the max limit and timeincrement.

FIG. 16 is a flow diagram of a method to recover a time source accordingto one embodiment. Processing 1600 may be performed by processing logicwhich may include software, hardware, or a combination thereof. Forexample, process 1600 may be performed by time source recovery module523 of FIG. 14. Referring to FIG. 16, at block 1601, processing logicdetermines a difference in time between a local time source and a timeof a GPS sensor. At block 1602, processing logic determines a max limitin difference and a max recovery increment (e.g., step increment) or amax recovery time interval for a smooth time source recovery. At block1603, processing logic determines that the difference between the localtime source and a time of the GPS sensor to be less than the max limit.At block 1604, processing logic plans a smooth recovery of the timesource to converge the local time source to a time of the GPS sensorwithin the max recovery time interval. At block 1605, processing logicgenerates a timestamp (RX/TX or trigger timestamp) based on therecovered time source to timestamp sensor data for a sensor unit of theADV.

In one embodiment, processing logic further determines that thedifference between the local time source and a time of the GPS sensor tobe greater than the max limit. Processing logic then plans an abruptrecovery of the time source to assign the local time source to be a timeof the GPS sensor. In one embodiment, processing logic further plans thesmooth recovery of the time source to converge the local time source toa time of the GPS sensor based on a predetermined time incrementdifferent from the max recovery increment.

In one embodiment, the predetermined time increment (or step) is asingle clock cycle interval for the sensor unit. In another embodiment,the single clock cycle interval is 10 nanoseconds. In one embodiment,the max limit and the recovery increment or max recovery time intervalare preconfigured by a user. In one embodiment, the difference in timebetween a local time source and a time of the GPS sensor is determinedupon detecting a signal recovery from the GPS sensor.

FIG. 17 is a block diagram illustrating an example of a data processingsystem which may be used with one embodiment of the disclosure. Forexample, system 1500 may represent any of data processing systemsdescribed above performing any of the processes or methods describedabove, such as, for example, perception and planning system 110 or anyof servers 103-104 of FIG. 1. System 1500 can include many differentcomponents. These components can be implemented as integrated circuits(ICs), portions thereof, discrete electronic devices, or other modulesadapted to a circuit board such as a motherboard or add-in card of thecomputer system, or as components otherwise incorporated within achassis of the computer system.

Note also that system 1500 is intended to show a high level view of manycomponents of the computer system. However, it is to be understood thatadditional components may be present in certain implementations andfurthermore, different arrangement of the components shown may occur inother implementations. System 1500 may represent a desktop, a laptop, atablet, a server, a mobile phone, a media player, a personal digitalassistant (PDA), a Smartwatch, a personal communicator, a gaming device,a network router or hub, a wireless access point (AP) or repeater, aset-top box, or a combination thereof. Further, while only a singlemachine or system is illustrated, the term “machine” or “system” shallalso be taken to include any collection of machines or systems thatindividually or jointly execute a set (or multiple sets) of instructionsto perform any one or more of the methodologies discussed herein.

In one embodiment, system 1500 includes processor 1501, memory 1503, anddevices 1505-1508 connected via a bus or an interconnect 1510. Processor1501 may represent a single processor or multiple processors with asingle processor core or multiple processor cores included therein.Processor 1501 may represent one or more general-purpose processors suchas a microprocessor, a central processing unit (CPU), or the like. Moreparticularly, processor 1501 may be a complex instruction set computing(CISC) microprocessor, reduced instruction set computing (RISC)microprocessor, very long instruction word (VLIW) microprocessor, orprocessor implementing other instruction sets, or processorsimplementing a combination of instruction sets. Processor 1501 may alsobe one or more special-purpose processors such as an applicationspecific integrated circuit (ASIC), a cellular or baseband processor, afield programmable gate array (FPGA), a digital signal processor (DSP),a network processor, a graphics processor, a communications processor, acryptographic processor, a co-processor, an embedded processor, or anyother type of logic capable of processing instructions.

Processor 1501, which may be a low power multi-core processor socketsuch as an ultra-low voltage processor, may act as a main processingunit and central hub for communication with the various components ofthe system. Such processor can be implemented as a system on chip (SoC).Processor 1501 is configured to execute instructions for performing theoperations and steps discussed herein. System 1500 may further include agraphics interface that communicates with optional graphics subsystem1504, which may include a display controller, a graphics processor,and/or a display device.

Processor 1501 may communicate with memory 1503, which in one embodimentcan be implemented via multiple memory devices to provide for a givenamount of system memory. Memory 1503 may include one or more volatilestorage (or memory) devices such as random access memory (RAM), dynamicRAM (DRAM), synchronous DRAM (SDRAM), static RAM (SRAM), or other typesof storage devices. Memory 1503 may store information includingsequences of instructions that are executed by processor 1501, or anyother device. For example, executable code and/or data of a variety ofoperating systems, device drivers, firmware (e.g., input output basicsystem or BIOS), and/or applications can be loaded in memory 1503 andexecuted by processor 1501. An operating system can be any kind ofoperating systems, such as, for example, Robot Operating System (ROS),Windows® operating system from Microsoft®, Mac OS®/iOS® from Apple,Android® from Google®, LINUX, UNIX, or other real-time or embeddedoperating systems.

System 1500 may further include IO devices such as devices 1505-1508,including network interface device(s) 1505, optional input device(s)1506, and other optional IO device(s) 1507. Network interface device1505 may include a wireless transceiver and/or a network interface card(NIC). The wireless transceiver may be a WiFi transceiver, an infraredtransceiver, a Bluetooth transceiver, a WiMax transceiver, a wirelesscellular telephony transceiver, a satellite transceiver (e.g., a globalpositioning system (GPS) transceiver), or other radio frequency (RF)transceivers, or a combination thereof. The NIC may be an Ethernet card.

Input device(s) 1506 may include a mouse, a touch pad, a touch sensitivescreen (which may be integrated with display device 1504), a pointerdevice such as a stylus, and/or a keyboard (e.g., physical keyboard or avirtual keyboard displayed as part of a touch sensitive screen). Forexample, input device 1506 may include a touch screen controller coupledto a touch screen. The touch screen and touch screen controller can, forexample, detect contact and movement or break thereof using any of aplurality of touch sensitivity technologies, including but not limitedto capacitive, resistive, infrared, and surface acoustic wavetechnologies, as well as other proximity sensor arrays or other elementsfor determining one or more points of contact with the touch screen.

IO devices 1507 may include an audio device. An audio device may includea speaker and/or a microphone to facilitate voice-enabled functions,such as voice recognition, voice replication, digital recording, and/ortelephony functions. Other IO devices 1507 may further include universalserial bus (USB) port(s), parallel port(s), serial port(s), a printer, anetwork interface, a bus bridge (e.g., a PCI-PCI bridge), sensor(s)(e.g., a motion sensor such as an accelerometer, gyroscope, amagnetometer, a light sensor, compass, a proximity sensor, etc.), or acombination thereof. Devices 1507 may further include an imagingprocessing subsystem (e.g., a camera), which may include an opticalsensor, such as a charged coupled device (CCD) or a complementarymetal-oxide semiconductor (CMOS) optical sensor, utilized to facilitatecamera functions, such as recording photographs and video clips. Certainsensors may be coupled to interconnect 1510 via a sensor hub (notshown), while other devices such as a keyboard or thermal sensor may becontrolled by an embedded controller (not shown), dependent upon thespecific configuration or design of system 1500.

To provide for persistent storage of information such as data,applications, one or more operating systems and so forth, a mass storage(not shown) may also couple to processor 1501. In various embodiments,to enable a thinner and lighter system design as well as to improvesystem responsiveness, this mass storage may be implemented via a solidstate device (SSD). However in other embodiments, the mass storage mayprimarily be implemented using a hard disk drive (HDD) with a smalleramount of SSD storage to act as a SSD cache to enable non-volatilestorage of context state and other such information during power downevents so that a fast power up can occur on re-initiation of systemactivities. Also a flash device may be coupled to processor 1501, e.g.,via a serial peripheral interface (SPI). This flash device may providefor non-volatile storage of system software, including BIOS as well asother firmware of the system.

Storage device 1508 may include computer-accessible storage medium 1509(also known as a machine-readable storage medium or a computer-readablemedium) on which is stored one or more sets of instructions or software(e.g., module, unit, and/or logic 1528) embodying any one or more of themethodologies or functions described herein. Processingmodule/unit/logic 1528 may represent any of the components describedabove, such as, for example, perception module 302, planning module 305,control module 306, and/or sensor unit 500. Processing module/unit/logic1528 may also reside, completely or at least partially, within memory1503 and/or within processor 1501 during execution thereof by dataprocessing system 1500, memory 1503 and processor 1501 also constitutingmachine-accessible storage media. Processing module/unit/logic 1528 mayfurther be transmitted or received over a network via network interfacedevice 1505.

Computer-readable storage medium 1509 may also be used to store the somesoftware functionalities described above persistently. Whilecomputer-readable storage medium 1509 is shown in an exemplaryembodiment to be a single medium, the term “computer-readable storagemedium” should be taken to include a single medium or multiple media(e.g., a centralized or distributed database, and/or associated cachesand servers) that store the one or more sets of instructions. The terms“computer-readable storage medium” shall also be taken to include anymedium that is capable of storing or encoding a set of instructions forexecution by the machine and that cause the machine to perform any oneor more of the methodologies of the present disclosure. The term“computer-readable storage medium” shall accordingly be taken toinclude, but not be limited to, solid-state memories, and optical andmagnetic media, or any other non-transitory machine-readable medium.

Processing module/unit/logic 1528, components and other featuresdescribed herein can be implemented as discrete hardware components orintegrated in the functionality of hardware components such as ASICS,FPGAs, DSPs or similar devices. In addition, processingmodule/unit/logic 1528 can be implemented as firmware or functionalcircuitry within hardware devices. Further, processing module/unit/logic1528 can be implemented in any combination hardware devices and softwarecomponents.

Note that while system 1500 is illustrated with various components of adata processing system, it is not intended to represent any particulararchitecture or manner of interconnecting the components; as suchdetails are not germane to embodiments of the present disclosure. Itwill also be appreciated that network computers, handheld computers,mobile phones, servers, and/or other data processing systems which havefewer components or perhaps more components may also be used withembodiments of the disclosure.

Some portions of the preceding detailed descriptions have been presentedin terms of algorithms and symbolic representations of operations ondata bits within a computer memory. These algorithmic descriptions andrepresentations are the ways used by those skilled in the dataprocessing arts to most effectively convey the substance of their workto others skilled in the art. An algorithm is here, and generally,conceived to be a self-consistent sequence of operations leading to adesired result. The operations are those requiring physicalmanipulations of physical quantities.

It should be borne in mind, however, that all of these and similar termsare to be associated with the appropriate physical quantities and aremerely convenient labels applied to these quantities. Unlessspecifically stated otherwise as apparent from the above discussion, itis appreciated that throughout the description, discussions utilizingterms such as those set forth in the claims below, refer to the actionand processes of a computer system, or similar electronic computingdevice, that manipulates and transforms data represented as physical(electronic) quantities within the computer system's registers andmemories into other data similarly represented as physical quantitieswithin the computer system memories or registers or other suchinformation storage, transmission or display devices.

Embodiments of the disclosure also relate to an apparatus for performingthe operations herein. Such a computer program is stored in anon-transitory computer readable medium. A machine-readable mediumincludes any mechanism for storing information in a form readable by amachine (e.g., a computer). For example, a machine-readable (e.g.,computer-readable) medium includes a machine (e.g., a computer) readablestorage medium (e.g., read only memory (“ROM”), random access memory(“RAM”), magnetic disk storage media, optical storage media, flashmemory devices).

The processes or methods depicted in the preceding figures may beperformed by processing logic that comprises hardware (e.g. circuitry,dedicated logic, etc.), software (e.g., embodied on a non-transitorycomputer readable medium), or a combination of both. Although theprocesses or methods are described above in terms of some sequentialoperations, it should be appreciated that some of the operationsdescribed may be performed in a different order. Moreover, someoperations may be performed in parallel rather than sequentially.

Embodiments of the present disclosure are not described with referenceto any particular programming language. It will be appreciated that avariety of programming languages may be used to implement the teachingsof embodiments of the disclosure as described herein.

In the foregoing specification, embodiments of the disclosure have beendescribed with reference to specific exemplary embodiments thereof. Itwill be evident that various modifications may be made thereto withoutdeparting from the broader spirit and scope of the disclosure as setforth in the following claims. The specification and drawings are,accordingly, to be regarded in an illustrative sense rather than arestrictive sense.

What is claimed is:
 1. A method to generate a time for an autonomousdriving vehicle (ADV), the method comprising: receiving, at a sensorunit, a global positioning system (GPS) pulse signal from a GPS sensorof the ADV, wherein the sensor unit is coupled to a plurality of sensorsmounted on the ADV and a host system, wherein the host system is toperceive a driving environment surrounding the ADV based on sensor dataobtained from the sensors and processed by a processing module of thesensor unit and to plan a path to autonomously drive the ADV based onperception data; receiving a first local oscillator signal from a localoscillator of the sensor unit; synchronizing the first local oscillatorsignal to the GPS pulse signal in real-time, including modifying thefirst local oscillator signal based on the GPS pulse signal; andgenerating a second oscillator signal based on the synchronized firstlocal oscillator signal, wherein the second oscillator signal isprovided to at least one of the sensors to be used as a clock signal tooperate the sensor unit, wherein synchronizing the first localoscillator signal comprises: generating a first counter having a firstgranularity using the local oscillator; and monitoring the first counterto count a number of oscillations at the first granularity to reach atime interval of the GPS pulse signal to synchronize the localoscillator at the first granularity, wherein each count represents anoscillation at the first granularity.
 2. The method of claim 1, whereinsynchronizing the first local oscillator signal further comprises:calculating a first count value based on the monitored first counter atthe first granularity; and modifying the first counter so that eachcount represents a fraction of an oscillation at the first granularitybased on the calculated first count value to synchronize the localoscillator at the first granularity.
 3. The method of claim 2, whereinsynchronizing the first local oscillator signal further comprises:generating a second counter having a second granularity using the localoscillator; monitoring the second counter to count a number ofoscillations at the second granularity to reach a time interval of theGPS pulse signal, wherein each count represents an oscillation at thesecond granularity; calculating a second count value based on themonitored second counter at the second granularity; and modifying thesecond counter so that each count represents a fraction of anoscillation at the second granularity based on the calculated secondcount value to synchronize the local oscillator at the secondgranularity.
 4. The method of claim 3, wherein synchronizing the firstlocal oscillator signal further comprises: generating a third counterhaving a third granularity using the local oscillator; monitoring thethird counter to count a number of oscillations at the third granularityto reach a time interval of the GPS pulse signal, wherein each countrepresents an oscillation at the third granularity; calculating a thirdcount value based on the monitored third counter at the thirdgranularity; and modifying the third counter so that each countrepresents a fraction of an oscillation at the third granularity basedon the calculated third count value to synchronize the local oscillatorat the third granularity.
 5. The method of claim 4, wherein the firstgranularity is a millisecond granularity, the second granularity is amicrosecond granularity, and the third granularity is a nanosecondgranularity.
 6. The method of claim 4, further comprising disabling thegeneration of any of the first, second, or the third counters forsynchronization.
 7. The method of claim 4, further comprisingmaintaining the first, second, and third count values if the GPS sensorsignal is lost, until the GPS sensor signal is once again regained.
 8. Acircuit to generate a time for an ADV, the circuit comprising: a monitorcircuit, wherein the monitor circuit is to receive a GPS pulse signalfrom a GPS sensor of the ADV, wherein the monitor circuit is part of asensor unit coupled to a plurality of sensors mounted on the ADV and ahost system, wherein the host system is to perceive a drivingenvironment surrounding the ADV based on sensor data obtained from thesensors and processed by a processing module of the sensor unit and toplan a path to autonomously drive the ADV based on perception data,wherein the monitor circuit is to receive a local oscillator signal froma local oscillator of the sensor unit; wherein the monitor circuitmonitors an oscillation for the local pulse signal; a synchronizationcircuit coupled to the monitor circuit, the synchronization circuit isto synchronize the first local oscillator signal to the GPS pulse signalin real-time, including modifying the first local oscillator signalbased on the GPS pulse signal, wherein the synchronization circuit is togenerate a second oscillator signal based on the synchronized firstlocal oscillator signal, wherein the second oscillator signal isprovided to at least one of the sensors to be used as a clock signal tooperate the sensor unit; and a first counter generator circuit coupledto the monitor and synchronization circuits, the first counter generatorcircuit to generate a first counter having a first granularity using thelocal oscillator, wherein the monitor circuit further monitors the firstcounter to count a number of oscillations at the first granularity toreach a time interval of the GPS pulse signal, wherein each countrepresents an oscillation at the first granularity.
 9. The circuit ofclaim 8, wherein the synchronization circuit is to calculate a firstcount value based on the monitored first counter at the firstgranularity and modifies the first counter so that each count representsa fraction of an oscillation at the first granularity based on thecalculated first count value to synchronize the local oscillator at thefirst granularity.
 10. The circuit of claim 9, further comprising: asecond counter generator circuit coupled to the monitor andsynchronization circuits, the second counter generator circuit togenerate a second counter having a second granularity using the localoscillator, wherein the monitor circuit further monitors the secondcounter to count a number of oscillations at the second granularity toreach a time interval of the GPS pulse signal, wherein each countrepresents an oscillation at the second granularity, and wherein thesynchronization circuit is to calculate a second count value based onthe monitored second counter at the second granularity and modifies thesecond counter so that each count represents a fraction of anoscillation at the second granularity based on the calculated secondcount value to synchronize the local oscillator at the secondgranularity.
 11. The circuit of claim 10, further comprising: a thirdcounter generator circuit coupled to the monitor and synchronizationcircuits, the third counter generator circuit to generate a thirdcounter having a third granularity using the local oscillator, whereinthe monitor circuit further monitors the third counter to count a numberof oscillations at the third granularity to reach a time interval of theGPS pulse signal, wherein each count represents an oscillation at thethird granularity, and wherein the synchronization circuit is tocalculate a third count value based on the monitored third counter atthe third granularity and modifies the third counter so that each countrepresents a fraction of an oscillation at the third granularity basedon the calculated third count value to synchronize the local oscillatorat the third granularity.
 12. The circuit of claim 11, wherein the firstgranularity is a millisecond granularity, the second granularity is amicrosecond granularity, and the third granularity is a nanosecondgranularity.
 13. The circuit of claim 11, further comprising: ade-multiplexer coupled to the first, second, and third counter generatorcircuits; and a configuration circuit coupled to the de-multiplexer togenerate a select signal to enable the first, second, or third countergenerator circuits.
 14. The circuit of claim 11, wherein thesynchronization circuit further maintains the first, second, and thirdcount values if monitor circuit no longer receives the GPS sensorsignal, until the GPS sensor signal is once again received.
 15. A sensorunit for an ADV, comprising: a sensor interface coupled to a pluralityof sensors mounted on a plurality of locations of the ADV; a hostinterface coupled to a host system, wherein the host system is toperceive a driving environment surrounding the ADV based on sensor dataobtained from the sensors and processed by a processing module of thesensor unit and to plan a path to autonomously drive the ADV based onperception data; and a time generation circuit coupled to the sensor andhost interfaces, the time generation circuit comprises: a monitorcircuit, wherein the monitor circuit is to receive a GPS pulse signalfrom a GPS sensor of the ADV, wherein the monitor circuit is to receivea local oscillator signal from a local oscillator of the sensor unit;wherein the monitor circuit monitors an oscillation for the local pulsesignal; a synchronization circuit coupled to the monitor circuit, thesynchronization circuit is to synchronize the first local oscillatorsignal to the GPS pulse signal in real-time, including modifying thefirst local oscillator signal based on the GPS pulse signal, wherein thesynchronization circuit is to generate a second oscillator signal basedon the synchronized first local oscillator signal, wherein the secondoscillator signal is provided to at least one of the sensors to be usedas a clock signal to operate the sensor unit; and a first countergenerator circuit coupled to the monitor and synchronization circuits,the first counter generator circuit to generate a first counter having afirst granularity using the local oscillator, wherein the monitorcircuit further monitors the first counter to count a number ofoscillations at the first granularity to reach a time interval of theGPS pulse signal, wherein each count represents an oscillation at thefirst granularity.
 16. The sensor unit of claim 15, wherein thesynchronization circuit is to calculate a first count value based on themonitored first counter at the first granularity and modifies the firstcounter so that each count represents a fraction of an oscillation atthe first granularity based on the calculated first count value tosynchronize the local oscillator at the first granularity.
 17. Thesensor unit of claim 16, further comprising: a second counter generatorcircuit coupled to the monitor and synchronization circuits, the secondcounter generator circuit to generate a second counter having a secondgranularity using the local oscillator, wherein the monitor circuitfurther monitors the second counter to count a number of oscillations atthe second granularity to reach a time interval of the GPS pulse signal,wherein each count represents an oscillation at the second granularity,and wherein the synchronization circuit is to calculate a second countvalue based on the monitored second counter at the second granularityand modifies the second counter so that each count represents a fractionof an oscillation at the second granularity based on the calculatedsecond count value to synchronize the local oscillator at the secondgranularity.
 18. The sensor unit of claim 17, further comprising: athird counter generator circuit coupled to the monitor andsynchronization circuits, the third counter generator circuit togenerate a third counter having a third granularity using the localoscillator, wherein the monitor circuit further monitors the thirdcounter to count a number of oscillations at the third granularity toreach a time interval of the GPS pulse signal, wherein each countrepresents an oscillation at the third granularity, and wherein thesynchronization circuit is to calculate a third count value based on themonitored third counter at the third granularity and modifies the thirdcounter so that each count represents a fraction of an oscillation atthe third granularity based on the calculated third count value tosynchronize the local oscillator at the third granularity.
 19. Thesensor unit of claim 18, wherein the first granularity is a millisecondgranularity, the second granularity is a microsecond granularity, andthe third granularity is a nanosecond granularity.
 20. The sensor unitof claim 18, further comprising: a de-multiplexer coupled to the first,second, and third counter generator circuits; and a configurationcircuit coupled to the de-multiplexer to generate a select signal toenable the first, second, or third counter generator circuits.
 21. Thesensor unit of claim 18, wherein the synchronization circuit furthermaintains the first, second, and third count values if monitor circuitno longer receives the GPS sensor signal, until the GPS sensor signal isonce again received.